Updated 7/21/2026

Language Model Security Database

Explore primary-source AI security research, evaluated models, attack techniques, and defensive evidence.

959 research findings · 1077 evaluated models

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Semantic Cache Collision Hijack

Updated 3/9/2026

Semantic caching mechanisms in LLM applications are vulnerable to cross-tenant cache key collision attacks (CacheAttack) due to the inherent mathematical conflict between locality-preserving fuzzy hashing and cryptographic collision resistance (the avalanche effect). An attacker can leverage gradient-based search algorithms to optimize an adversarial discrete suffix that, when appended to a malicious prompt, forces its output embedding vector to collide with the embedding of a targeted benign…

From Similarity to Vulnerability: Key Collision Attack on LLM Semantic Caching

Affects: Llama 3.1 8B, Mistral 7B, DeepSeek R1

Source: arXiv

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.